Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Face Mask Detector for Raspberry Pi Based on Computer Vision and Edge Computing

The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China's largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost. © 2023 ACM.

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency, short response time, and optimal energy consumption. In this paper, the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models. The proposed models were validated using k cross-validation to ensure the consistency of models. Based on the experimental results, our proposed models have recorded good accuracies with highest of 97.767% by Support Vector Machine. According to the findings of experiments, the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects, as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

A Wireless Virtual Reality-Based Multimedia-Assisted Teaching System Framework under Mobile Edge Computing

In recent years, virtual reality (VR) has gradually entered the daily education and teaching activities from pure scientific research. In the area of assistance teaching, some typical computer softwares still play some important roles. This makes remote teaching activities can just learn voice, yet cannot possess the feeling of realistic existence. Especially in scenario of COVID-19, remote teaching activities with proper perceptibility are in urgent demand. To deal with the current challenge, this paper proposes a wireless VR-based multimedia-assisted teaching system framework under mobile edge computing networks. In this framework, cooperative edge caching and adaptive streaming based on viewport prediction are adopted to jointly improve the quality of experience (QoE) of VR users. First, we investigated the resource management problem of caching and adaptive streaming in this framework. Considering the complexity of the formulated problem, a distributed learning scheme is proposed to solve the problem. The experimental data are verified and the experimental results prove that the studied methods improve the performance of user QoE. [ FROM AUTHOR] Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.

A review of edge computing in healthcare Internet of things: Theories, practices and challenges

The pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyze data that can signify a quantitative revolution in healthcare as devices become more convenient and smaller data become larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process [15, 16]. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources [17]. Finally, necessity of edge computing in era of Covid-19 pandemic is addressed. © The Institution of Engineering and Technology 2022.

A game theory-based COVID-19 close contact detecting method with edge computing collaboration.

People all throughout the world have suffered from the COVID-19 pandemic. People can be infected after brief contact, so how to assess the risk of infection for everyone effectively is a tricky challenge. In view of this challenge, the combination of wireless networks with edge computing provides new possibilities for solving the COVID-19 prevention problem. With this observation, this paper proposed a game theory-based COVID-19 close contact detecting method with edge computing collaboration, named GCDM. The GCDM method is an efficient method for detecting COVID-19 close contact infection with users' location information. With the help of edge computing's feature, the GCDM can deal with the detecting requirements of computing and storage and relieve the user privacy problem. Technically, as the game reaches equilibrium, the GCDM method can maximize close contact detection completion rate while minimizing the latency and cost of the evaluation process in a decentralized manner. The GCDM is described in detail and the performance of GCDM is analyzed theoretically. Extensive experiments were conducted and experimental results demonstrate the superior performance of GCDM over other three representative methods through comprehensive analysis.

Auto-detection of social contact for fighting COVID-19 pandemic.

Because of the high infectivity of COVID-19, it is essential to detect the close contacts of patients as soon as possible to contain the outbreak of the epidemic. However, due to the level of technological development, the current methods and research on contact detection require manual participation. This paper proposes a future oriented automation method, which uses mobile agents loaded on sensing devices and edge coordinators to form a multi-agent system on the street. Based on perception, tracking and edge-computing, the contact probability between infected people and pedestrians is estimated. A series of simulations provide the comparison of parameters in application deployment. The simulation results show that the proposed street expropriation mode and edge-computing algorithm can further improve the detection rate. (English) [ FROM AUTHOR] 由于新冠病毒的高传染性，及早发现患者的密切接触者对于遏制疫情爆发至关重要。而受限于技术发 展的水平，目前关于接触检测的方法和研究均需人工参与。提出了一种面向未来的自动化方法，利用加载在感 知设备上的移动智能体和边缘协调器在街道上组成多智能体系统，基于对感染者的感知、跟踪和边缘计算，实现 了感染者与行人之间的接触概率估算。系列仿真给出了应用部署中的参数比较。仿真结果表明，提出的街道征 用模式及边缘计算算法可以进一步改善检测率。 (Chinese) [ FROM AUTHOR] Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.

Preschool education optimization based on mobile edge computing under COVID‐19

The COVID‐19 pandemic has brought profound changes in people's live and work. It has also accelerated the development of education from traditional model to online model, which is particularly important in preschool education. Preschoolers communicate with teachers through online video, so how to provide high quality and low latency online teaching has become a new challenge. In cloud computing, users offload computing tasks to the cloud to meet the high computing demands of their devices, but cloud‐based solutions have led to huge bandwidth usage and unpredictable latency. In order to solve this problem, mobile edge computing (MEC) deploys the server at the edge of the network to provide the service with close range and low latency. In task scheduling, edge computing (EC) devices have rational thinking, and they are unwilling to collaborate with MEC server to perform tasks due to their selfishness. Therefore, it is necessary to design an effective incentive mechanism to encourage the collaboration of EC devices. Through analysis of MEC server and EC devices, we propose a distributed task scheduling algorithm—Stackelberg game approach based on alternating direction method of multipliers, which selects the appropriate incentive mechanism to encourage the collaboration of EC devices. The experimental results demonstrate that the proposed approach can rapidly converge to a certain accuracy within 40 iterations, and in incentive mechanism comparison and quality of experience, the proposed approach also has a good performance in anti‐jitter and low latency. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Research and Application of 5G Edge AI in Medical Industry

With the development of 5G and AI technology, the infectious virus detection framework system based on the combination of 5G MEC and medical sensors can effectively assist in the intelligent detection and control of influenza viruses such as COVID-19. Employing the edge computing and 5G+MEC model, the virus AI model is trained for the collected influenza virus data. Then the virus AI model can be used to evaluate the virus patients on the local edge computing service platform. Therefore, this paper introduces an algorithm and resource allocation, which uses 5G functions (especially, low latency, high bandwidth, wide connectivity, and other functions) to achieve local chest X-ray or CT scan images to detect COVID-19. Meanwhile, this paper also compares the computational efficiency of different algorithms in the 5G edge AI-based infectious virus detection framework, in this way to select the best algorithm and resource allocation. © 2022 IEEE.

Applicability of edge computing paradigm for Covid-19 mitigation

With lockdowns and overburdened medical facilities during the Covid-19 pandemic, technology and computing paradigms play a vital role in providing remote healthcare solutions. We assess as how the existing computing paradigms could be deployed to prevent the spread of the disease, expedite the diagnosis, and facilitate remote monitoring of patients to reduce the burden on the overstretched medical facilities. The chapter will include a literature survey based on the articles published in but not limited to Science Direct, Google Scholar, Research Gate, and PubMed. This study weighs the pros and cons of using different paradigms in diverse scenarios and provides recommendations for efficient healthcare solutions. The chapter also focuses on the issues related to edge computing, such as resource provisioning, energy preservation, etc. In this era of technology, edge computing can be used to enhance the efficacy of healthcare solutions without burdening healthcare professionals and facilities. In this chapter, experimentation will focus on deploying intelligent techniques in the edge computing paradigm. © 2023 Elsevier Inc. All rights reserved.

CampusTrace: A Framework for Centralized Contact Tracing System for Universities in Preparation for Face-to-Face Classes

The government addresses that one of the biggest problems in the country is lacking an effective contact tracing solution. The Philippines' current contact tracing systems have encountered a lot of challenges because of the lack of features that would ensure safety and awareness to users around. The study aims to propose a system framework that can be used as Contact Tracing Solution using data warehousing and edge computing would improve the tracing in small and concentrated areas such as universities and offices. The researchers gather reviews and studies on how to develop a system that would address the current problem in the contact tracing scenario in the Philippines, particularly in the education field. The researcher will be going to apply the descriptive and development design for the conduct of the study and the waterfall methodology will be the software model for the development of the proposed system. Therefore, it is better to develop a contact tracing application that will be used by universities whose main objective is to spread awareness to potentially close contacts of a COVID-19 positive case and further implement the system framework to provide a proactive solution for contact tracing in the academe. © 2022 IEEE.

Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning

The global spread of COVID-19 highlights the urgency of quickly finding drugs and vaccines and suggests that similar challenges will arise in the future. This underscores the need for ongoing efforts to overcome the obstacles involved in the development of potential treatments. Although some progress has been made in the use of Artificial Intelligence (AI) in drug discovery, virologists, pharmaceutical companies, and investors seek more long-term solutions and greater investment in emerging technologies. One potential solution to aid in the drug-development process is to combine the capabilities of the Internet of Medical Things (IoMT), edge computing (EC), and deep learning (DL). Some practical frameworks and techniques utilizing EC, IoMT, and DL have been proposed for the monitoring and tracking of infected individuals or high-risk areas. However, these technologies have not been widely utilized in drug clinical trials. Given the time-consuming nature of traditional drug- and vaccine-development methods, there is a need for a new AI-based platform that can revolutionize the industry. One approach involves utilizing smartphones equipped with medical sensors to collect and transmit real-time physiological and healthcare information on clinical-trial participants to the nearest edge nodes (EN). This allows the verification of a vast amount of medical data for a large number of individuals in a short time frame, without the restrictions of latency, bandwidth, or security constraints. The collected information can be monitored by physicians and researchers to assess a vaccine's performance.

A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE

Discovering AI-enabled convergences based on BERT and topic network

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-Associated research s collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective. © 2023 Korean Society for Internet Information. All rights reserved.

The development of new remote technologies in disaster medicine education: A scoping review.

Background: Remote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education. Objectives: The aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years. Literature scoping reviews: Mobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education. Methods: We performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022. Results: A total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems. Discussions: Artificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.

Cognitive Cameras on the Edge for Crowd Physical Distancing Monitoring in the Covid-19 Era.

With the rise of the Internet of Things (IoT) architectures and protocols, new video analytics systems and surveillance applications have been developed. In conventional systems, all the streams produced by cameras are sent to a centralized node where they can be seen by human operators whose task is to identify uncommon on abnormal situations. However, this way, much bandwidth is necessary for the system to work, and the number of necessary resources is proportional to the number of cameras and streams involved. In this paper, we propose an interesting approach to this problem: transforming any IP camera into a cognitive object. A cognitive camera (CC) can be considered a classic connected camera with onboard computational power for intelligent video processing. A CC can understand and interact with the surroundings, intelligently analyze complex scenes, and interact with the users. The IoT Edge Computing approach decreases latency in the decision-making process and consumes a tiny portion of bandwidth concerning the stream of a video, even in low resolution. CCs can help to address COVID-19. As a preventive measure, proper crowd monitoring and management systems must be installed in public places to limit sudden outbreaks and improve healthcare. The number of new infections can be significantly reduced by adopting physical distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for physical distance classification using CCs is proposed in this research paper. The experiment on Movidius board, an AI accelerator device, provides promising results of our proposed method in which the accuracies can achieve more than 85% from different datasets.